Oxidized and Unsaturated: Key Organic Aerosol Traits Associated with Cellular Reactive Oxygen Species Production in the Southeastern United States

Exposure to ambient fine particulate matter (PM2.5) is associated with millions of premature deaths annually. Oxidative stress through overproduction of reactive oxygen species (ROS) is a possible mechanism for PM2.5-induced health effects. Organic aerosol (OA) is a dominant component of PM2.5 worldwide, yet its role in PM2.5 toxicity is poorly understood due to its chemical complexity. Here, through integrated cellular ROS measurements and detailed multi-instrument chemical characterization of PM in urban southeastern United States, we show that oxygenated OA (OOA), especially more-oxidized OOA, is the main OA type associated with cellular ROS production. We further reveal that highly unsaturated species containing carbon–oxygen double bonds and aromatic rings in OOA are major contributors to cellular ROS production. These results highlight the key chemical features of ambient OA driving its toxicity. As more-oxidized OOA is ubiquitous and abundant in the atmosphere, this emphasizes the need to understand its sources and chemical processing when formulating effective strategies to mitigate PM2.5 health impacts.


Online measurements
HR-ToF-AMS: A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS, Aerodyne Research Inc.) was coupled with a PM2.5 cyclone facing west at 4.5 m above the ground.
A nafion dryer was placed upstream of the instrument to dry the particles below relative humidity (RH) of 20% in order to minimize the influence of RH on the collection efficiency (CE). 1 The composition-dependent CE correction was applied following the procedure in Middlebrook et al. 2 .Detailed description of the instrument can be found from previous studies [3][4][5][6] .Ionization efficiency (IE) calibrations were performed on a weekly basis with 300 nm ammonium nitrate and ammonium sulfate.The average sample time was set at 1 min, and the data analysis was performed in Igor Pro 6.34 (WaveMetrics Inc.) using Squirrel v1.53 and PIKA v1.12.
Positive Matrix Factorization (PMF) analysis was performed on the high-resolution organic mass spectra (m/z 12-150) using PMF Evaluation Toolkit (PET v.2.0x) to determine the sources of organic aerosol.PMF analysis deconvolves the organic data matrix as a linear combination of various factors with constant organic mass spectra but varying concentrations across the dataset. 7,8 Th details of preprocessing of organic data and error matrices are described in Joo et al. 3 .Five OA types were identified in summer (MO-OOA, LO-OOA, HOA, COA, and Isoprene-OA) and winter (MO-OOA, LO-OOA, HOA, COA, and BBOA), respectively. 3,4,9,10 Brefly, MO-OOA and LO-OOA were characterized by dominant signals at m/z 44 (CO2 + ) and m/z 43 (C2H3O + ), with MO-OOA having a higher degree of oxidation.HOA was distinguished by alkyl fragments signatures and rush hour peaks.COA was determined by prominent signals at m/z 41 (C3H5 + ) and m/z 55 (C4H7 + ), and higher m/z 55 to 57 ratio compared to the other OA types.Isoprene-OA was characterized by dominant signals at m/z 53 (C4H5 + ) and m/z 82 (C5H6O + ).BBOA was characterized by enhanced signals at m/z 60 (C2H4O2 + ) and m/z 73 (C3H5O2 + ), which are major fragment ions for levoglucosan.
Note that the instrument does not measure black carbon.While black carbon has also been suggested to play a role in adverse health effects of PM2.5, [11][12][13][14][15] its role is not considered as our work is mainly determining cellular ROS production for water-soluble PM2.5.

FIGAERO-CIMS:
][18] Gas-phase measurements took place for 30 min, during which ambient aerosol was collected onto a Teflon filter (Pall).Ambient air was sampled through a 1 µm cyclone (URG) at 16.7 L/min, and was then subsampled at ~6.8 L/min through the filter for aerosol collection.After 30-min collection, the filter port was moved to the CIMS inlet and heated dry N2 gas flowed through the filter to vaporize collected aerosols on the filter.Vaporized compounds were then carried into the CIMS for measurements.The desorption period lasted for 30 min; temperature of N2 gas increased linearly from ~30 to 200 °C for 15 min during the ramping stage, stayed at 200 °C for 10 min during the soaking stage, and cooled back to 30 °C for 5 min during the cooling stage.The sum thermograms during summer and winter sampling are provided in Figure S10.In total, one complete measurement cycle took place every hour.Once every 7 cycles, ambient air was sampled through an additional filter prior to the filter collection to serve as particle-phase background measurements.Raw signal data were pre-averaged to 10-s data and were further analyzed using Tofware v2.5.11 in Igor Pro 6. 19 Signals for particle-phase measurements were integrated area during the desorption period with background subtraction.

Offline measurements
LC-MS/MS analysis: The procedures of LC-MS/MS analysis are as follows: for liquid chromatography, Milli-Q water with 0.1% acetic acid (A) and methanol (B) at 0.2 mL/min were used as mobile phases with an Agilent Poroshell 120 SQ-Aq reverse phase column (2.1×50 mm, 2.7 μm particle size).The following solvent gradient was used: from 0 to 2 min, 95% A and 5% B; from 2 to 22 min, increase B to 90%; from 22 to 27 min, hold at 90% B; then decrease to 5% B to prepare for the next run.Electrospray source parameters were set to the following: drying gas temperature of 225 °C and flow of 17 L/min, fragmentor voltage of 365 V, capillary voltage of 4000 V, sheath gas temperature of 400 °C and flow of 12 L/min, nebulizer pressure at 20 psig.All ions from initial sample runs without MS/MS were investigated with MS/MS, at collision energies of 5, 10, 20, 30, and 40 V. MS/MS spectra were analyzed with SIRIUS and CSI:FingerID 20,21 to identify molecular structural features and functional groups.Finally, we used the APRL Substructure Search program 22 to enumerate atmospherically-relevant functional groups from the top scoring candidates exported from SIRIUS.Further methods details, including QA/QC, are described in Ditto et al. 23 .
FT-IR spectrometry analysis: The collected filters were analyzed using a Bruker Tensor II FT-IR spectrometer (Bruker Optics, Inc.), operated in transmission mode, with a liquid-nitrogencooled mercury cadmium telluride detector.The custom-built sample chamber within the spectrometer 24 was flushed continuously with air scrubbed of H2O and CO2 (model VCDA air purge system, Puregas, LLC, <10 % RH).Spectra included the wavenumber range of 4000 to 1500 cm −1 .Additional details about the FT-IR spectrometry analysis have been described in Takahama et al. 25 .Functional group concentrations were quantified in the ambient samples by applying multivariate regression coefficients, which were developed previously 26 , to each of the sample FT-IR spectra.The quantified functional groups included aliphatic carbon (aCH), carboxylic acids (COOH), oxalates (oxOCO, representing carboxylates), non-acid and non-oxalate carbonyls (naCO), and alcohols (aCOH).Each sample functional group mass was normalized by the volume of air collected during its 8-hr or 24-hr sample period.The method detection limits (MDLs) were calculated as the 95 th percentile minus the median of all blank filter functional group quantities, following the Interagency Monitoring of Protected of Visual Environments (IMPROVE) network Standard Operating Procedure 351.

Calculation of standardized regression coefficients (β) of MLRM-resolved predictors
The following equation was used to calculate standardized regression coefficients (β) of MLRM-resolved predictors: where  ! and  # ! are the standardized regression coefficient and unstandardized regression coefficient of predictor i, respectively;  ! is the standard deviation of the MLRM-input data of predictor i;  " is the standard deviation of the MLRM-input data of the dependent variable, i.e., cellular ROS in this work.

LC-MS/MS data
The LC-MS/MS analysis yielded information on both molecular formula and functional groups for the identified compounds 23 .Thus, the OSC of an individual compound was calculated based on the identified N-containing and S-containing functional groups.If there were > 1 types of N-containing or S-containing functional groups, an averaged N or S oxidation state was used.For example, if 1 amine (OSC of -3) and 1 organic nitrate (OSC of +5) group were in a compound, the overall oxidation state would be +1 for N.If N-containing or S-containing functional groups were not identified in some compounds, an oxidation state of 0 was used (for N or S).Note that the compounds with unidentified N-containing or S-containing functional groups account for < 14% of the total compounds.Thus, the OSC results shown in Figure 3 are not affected much by these compounds.
The abundance-weighted mean value of OSC (̅ ) was calculated using the following equation: where n is the number of identified compounds to be averaged, and wi and xi is the abundance and OSC of i compound, respectively.
The standard deviation of the abundance-weighted average OSC (SDw) was calculated as follows:

FIGAERO-CIMS data
The FIGAERO-CIMS yielded molecular formulas for each identified compound but without functional group information.To calculate the OSC of each compound, we used a lower bound of N oxidation state of -0.7 and S oxidation state of +2.3, computed by taking the average oxidation state of all the LC-MS/MS identified compounds containing N or S, respectively.The highest possible oxidation state of N and S, i.e., +5 and +6, were used as the upper bound.This is based on both positive and negative modes of ionization were used in LC-MS/MS and N and S with negative oxidation states were more likely detected in positive mode.But only negative mode of ionization (iodide ionization method) was used in CIMS.Thus, the averaged N and S oxidation states obtained from LC-MS/MS results would be reasonable as the lower bound to calculate OSC for FIGAERO-CIMS data.Thus, the following equations were used to calculate OSC of individual compounds in FIGAERO-CIMS data: For lower bound: For upper bound: The abundance-weighted mean values and standard deviation of OSC were calculated using equation (S2) and (S3).Figures S6 (A-B) and S6 (C-D) are the lower bound and upper bound OSC results, respectively.
Note that the OSC values for LC-MS/MS and FIGAERO-CIMS data were calculated for four groups of identified compounds according to their degree of unsaturation normalized by carbon number (DU/C).For a formula CxHyOzNiSj, DU/C was calculated as following:

FT-IR analysis and MLRM results
We employed FT-IR spectrometry to measure organic functional groups for the wintertime PM2.5 samples.The quantified functional groups include aliphatic carbon (aCH), carboxylic acids (COOH), oxalates (oxOCO, representing carboxylates), non-acid and non-oxalate carbonyls (naCO), and alcohols (aCOH).The mass concentrations of functional groups measured by FT-IR account for 79% of OA mass concentration measured by AMS (Figure S11), demonstrating these are dominant functional groups in OA.
We used MLRM to gain quantitative insights into the contribution of each functional group to OOA (Table S5, Figure 3C; results for all AMS-identified OA types and total AMS OA are shown in Figure S12).The contributions from aCH, COOH, oxOCO, and naCO are captured by the model, where positive regression coefficients are found for COOH, oxOCO, and naCO, and a negative regression coefficient is found for aCH.The negative association between aCH and OOA could reflect that the relative abundance of aCH would decrease when more OA is in the form of OOA.
Additional functional groups such as aromatics, organonitrates, and peroxides are not included in the MLRM analysis because FT-IR calibrations to measure these functional groups have not been developed.Measurements of these functional groups on filter samples using FT-IR have been proved challenging due to the low absorbance and varying peak shapes of aromatics 28 , stability of peroxides, 29 and stability and overlapping peaks for organonitrates. 30The unidentified functional groups by FT-IR may also contribute to OOA, which should be further explored in future work.S6.The five functional groups are measured by FT-IR spectrometry.Table S3.The ANOVA results of OSc of compounds in each DU/C group.

1. 3 .
Calculation methods of carbon oxidation state from LC-MS/MS data, FIGAERO-CIMS data, and AMS-identified OA factors C, H, O, N, and S are the possible elements in the molecules or fragments identified by the mass spectrometry techniques used in this study.In general, to accurately calculate carbon oxidation state (OSC), we used the formula OSC = 2×(O/C) -1×(H/C) -a×(N/C) -b×(S/C), where a and b are the oxidation states of N and S, respectively, building off of common approaches using O/C and H/C in the literature.27 (A-B), and the OSC results from equation (S5) were used in Figure S6 (C-D).

Figure S1 .Figure S2 .Figure S3 .
Figure S1.Representative dose response curve of ROS produced upon exposure to ambient PM2.5 (sampling date: 2018-02-07).ROS is expressed as fold increase over control cells, defined as probetreated cells incubated with stimulant-free media.Dose is expressed as mass in extract (µg).Data shown are means ± standard error of triplicate exposure experiments.The Hill equation was used to fit the dose-response curve and the area under the dose response curve (AUC) is shaded.

Figure S4 .
Figure S4.Standardized regression coefficients from MLRM analysis by using AMS-identified OA (A) and ACSM-identified OA (B).The AMS and ACSM data are collected from the same sampling site and the same period.

Figure S5 .
Figure S5.The fractions of compounds of different carbon numbers in each DU/C group from LC-MS/MS and FIGAERO-CIMS analyses.The LC-MS/MS and FIGAERO-CIMS employ different ionization methods (Materials and Methods), whose sensitivity varies across compound types.This is also reflected by differences of carbon number fractions in the DU/C = 0 group, i.e., C>9 compounds are dominant in LC-MS/MS data while C1-5 compounds exhibit the highest fraction in FIGAERO-CIMS data.However, both techniques show the highest fraction of C>9 compounds in the 0 < DU/C < 0.25 and 0.25 ≤ DU/C < 0.5 groups, and a larger contribution of C≤9 compounds in the DU/C ≥ 0.5 group.(DU/C: degree of unsaturation normalized by carbon number).

Figure S7 .
Figure S7.Degree of unsaturation normalized by carbon number (DU/C) values for the top fifty compounds most strongly correlated with MO-OOA and LO-OOA from the FIGAERO-CIMS data.Data are rank ordered by Pearson R with demarcations for DU/C values for 0.25 and 0.5.

Figure S8 .
Figure S8.Clustergrams of cellular ROS and selected PM components.Summer (A) and winter (B).This figure is a duplicate version of Figure 1 but with R values on the squares.

Figure S9 .Figure S10 .
Figure S9.Clustergram of cellular ROS and selected PM components using the combined data from summer and winter.Isoprene-OA and BBOA are not identified in winter and summer, respectively, so their values in the corresponding season are necessarily set to "0" to perform the combined multi-season cluster analysis.

Figure S11 .
Figure S11.Mass concentrations of OA analyzed by FT-IR spectrometry (stacked by identified functional groups) and AMS (averaged OA mass concentration).

Figure S12 .
Figure S12.Standardized regression coefficients of five functional groups for AMS-identified OA types and total AMS OA in winter data.The MLRM results and unstandardized regression coefficients are displayed in TableS6.The five functional groups are measured by FT-IR spectrometry.

Table S1 .
Summary of online measurements and offline filter sampling.

Table S2 .
The output values of the tolerance and variance inflation factor (VIF) of the MLRM-

Table S4 .
Mass concentrations of AMS-identified OA types and five transition metals (Fe, Cu, Mn, Cr, and Zn).

Table S5 .
The MLRM-resolved regression coefficients and standard deviations of each independent variable for OOA, AMS-identified OA types, and total AMS OA during winter.Multiple linear regression equations: OOA, or AMS-identified OA types, or total AMS OA = a×F1 + b×F2 + c×F3… + intercept, where F1, F2, F3 (etc.) are mass concentrations of the five functional groups measured by FT-IR spectrometry, and a, b, c (etc.) are the corresponding coefficients with arbitrary units.

Table S6 .
The carbon oxidation state (OSC) of AMS-identified OA types.